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OBJECTIVE Sulfonylureas, the first available drugs for the management of type 2 diabetes, remain widely prescribed today. However, there exists significant variability in glycemic response to treatment. We aimed to establish heritability of sulfonylurea response and identify genetic variants and interacting treatments associated with HbA1c reduction. RESEARCH DESIGN AND METHODS As an initiative of the Metformin Genetics Plus Consortium (MetGen Plus) and the DIabetes REsearCh on patient straTification (DIRECT) consortium, 5,485 White Europeans with type 2 diabetes treated with sulfonylureas were recruited from six referral centers in Europe and North America. We first estimated heritability using the generalized restricted maximum likelihood approach and then undertook genome-wide association studies of glycemic response to sulfonylureas measured as HbA1c reduction after 12 months of therapy followed by meta-analysis. These results were supported by acute glipizide challenge in humans who were naïve to type 2 diabetes medications, cis expression quantitative trait loci (eQTL), and functional validation in cellular models. Finally, we examined for possible drug-drug-gene interactions. RESULTS After establishing that sulfonylurea response is heritable (mean ± SEM 37 ± 11%), we identified two independent loci near the GXYLT1 and SLCO1B1 genes associated with HbA1c reduction at a genome-wide scale (P < 5 × 10−8). The C allele at rs1234032, near GXYLT1, was associated with 0.14% (1.5 mmol/mol), P = 2.39 × 10−8), lower reduction in HbA1c. Similarly, the C allele was associated with higher glucose trough levels (β = 1.61, P = 0.005) in healthy volunteers in the SUGAR-MGH given glipizide (N = 857). In 3,029 human whole blood samples, the C allele is a cis eQTL for increased expression of GXYLT1 (β = 0.21, P = 2.04 × 10−58). The C allele of rs10770791, in an intronic region of SLCO1B1, was associated with 0.11% (1.2 mmol/mol) greater reduction in HbA1c (P = 4.80 × 10−8). In 1,183 human liver samples, the C allele at rs10770791 is a cis eQTL for reduced SLCO1B1 expression (P = 1.61 × 10−7), which, together with functional studies in cells expressing SLCO1B1, supports a key role for hepatic SLCO1B1 (encoding OATP1B1) in regulation of sulfonylurea transport. Further, a significant interaction between statin use and SLCO1B1 genotype was observed (P = 0.001). In statin nonusers, C allele homozygotes at rs10770791 had a large absolute reduction in HbA1c (0.48 ± 0.12% [5.2 ± 1.26 mmol/mol]), equivalent to that associated with initiation of a dipeptidyl peptidase 4 inhibitor. CONCLUSIONS We have identified clinically important genetic effects at genome-wide levels of significance, and important drug-drug-gene interactions, which include commonly prescribed statins. With increasing availability of genetic data embedded in clinical records these findings will be important in prescribing glucose-lowering drugs.

In this paper we employ quantitative measurements of cognitive vulnerability triggers in phishing emails to predict the degree of success of an attack. To achieve this we rely on the cognitive psychology literature and develop an automated and fully quantitative method based on machine learning and econometrics to construct a triaging mechanism built around the cognitive features of a phishing email; we showcase our approach relying on data from the anti-phishing division of a large financial organization in Europe. Our evaluation shows empirically that an effective triaging mechanism for phishing success can be put in place by response teams to effectively prioritize remediation efforts (e.g. domain takedowns), by first acting on those attacks that are more likely to collect high response rates from potential victims., Comment: To appear in USENIX Security 2019

Background SNP panels that uniquely identify an individual are useful for genetic and forensic research. Previously recommended SNP panels are based on DNA profiles and mostly contain intragenic SNPs. With the increasing interest in RNA expression profiles, we aimed for establishing a SNP panel for both DNA and RNA-based genotyping. Results To determine a small set of SNPs with maximally discriminative power, genotype calls were obtained from DNA and blood-derived RNA sequencing data belonging to healthy, geographically dispersed, Dutch individuals. SNPs were selected based on different criteria like genotype call rate, minor allele frequency, Hardy–Weinberg equilibrium and linkage disequilibrium. A panel of 50 SNPs was sufficient to identify an individual uniquely: the probability of identity was 6.9 × 10− 20 when assuming no family relations and 1.2 × 10− 10 when accounting for the presence of full sibs. The ability of the SNP panel to uniquely identify individuals on DNA and RNA level was validated in an independent population dataset. The panel is applicable to individuals from European descent, with slightly lower power in non-Europeans. Whereas most of the genes containing the 50 SNPs are expressed in various tissues, our SNP panel needs optimization for other tissues than blood. Conclusions This first DNA/RNA SNP panel will be useful to identify sample mix-ups in biomedical research and for assigning DNA and RNA stains in crime scenes to unique individuals. Electronic supplementary material The online version of this article (10.1186/s12864-018-4482-7) contains supplementary material, which is available to authorized users.

Aims This study aimed to establish whether higher levels of glycated haemoglobin (HbA1c) are associated with increased sudden cardiac arrest (SCA) risk in non-diabetic individuals. Methods and results Case–control study in non-diabetic individuals (HbA1c < 6.5%) in the Netherlands. Cases were SCA patients with electrocardiogram (ECG)-documented ventricular fibrillation (VF, the predominant cause of SCA) and HbA1c measurements immediately after VF, prospectively included in September 2009–December 2012. Controls (up to 10 per case) were age/sex-matched non-SCA individuals, included in July 2006–November 2007. We studied 306 cases (56.4 ± 6.8 years, 79.1% male) and 1722 controls (54.0 ± 6.8 years, 64.8% male). HbA1c levels were higher in cases than in controls (5.8 ± 0.3% vs. 5.4 ± 0.3%, P < 0.001). The proportion of increased HbA1c (≥5.7%) was 63.1% in cases and 19.3% in controls (P < 0.001). Multivariate regression models indicated that increased HbA1c was associated with a > six-fold increased VF risk [adjusted odds ratio (ORadj) 6.74 (5.00–9.09)] and that 0.1% increase in HbA1c level was associated with 1.4-fold increase in VF risk, independent of concomitant cardiovascular risk factors. Increased VF risk at higher HbA1c is associated with acute myocardial infarction (MI) as cause of VF [OR 1.14 (1.04–1.24)], but the association between HbA1c and VF was similar in non-MI patients [OR 1.32 (1.21–1.44)] and MI patients [OR 1.47 (1.37–1.58)]. Conclusion Among non-diabetic individuals, risk of VF increased with rising HbA1c levels, independent of concomitant cardiovascular disease. Future studies should establish whether HbA1c level may be used as biomarker to recognize individuals at risk for VF.

Aims This study aimed to establish whether higher levels of glycated haemoglobin (HbA1c) are associated with increased sudden cardiac arrest (SCA) risk in non-diabetic individuals. Methods and results Case–control study in non-diabetic individuals (HbA1c P P  six-fold increased VF risk [adjusted odds ratio (ORadj) 6.74 (5.00–9.09)] and that 0.1% increase in HbA1c level was associated with 1.4-fold increase in VF risk, independent of concomitant cardiovascular risk factors. Increased VF risk at higher HbA1c is associated with acute myocardial infarction (MI) as cause of VF [OR 1.14 (1.04–1.24)], but the association between HbA1c and VF was similar in non-MI patients [OR 1.32 (1.21–1.44)] and MI patients [OR 1.47 (1.37–1.58)]. Conclusion Among non-diabetic individuals, risk of VF increased with rising HbA1c levels, independent of concomitant cardiovascular disease. Future studies should establish whether HbA1c level may be used as biomarker to recognize individuals at risk for VF.